Squares of different sizes: effect of geographical projection on model parameter estimates in species distribution modeling

Abstract

In species distribution analyses, environmental predictors and distribution data for large spatial extents are often available in long‐lat format, such as degree raster grids. Long‐lat projections suffer from unequal cell sizes, as a degree of longitude decreases in length from approximately 110 km at the equator to 0 km at the poles. Here we investigate whether long‐lat and equal‐area projections yield similar model parameter estimates, or result in a consistent bias. We analyzed the environmental effects on the distribution of 12 ungulate species with a northern distribution, as models for these species should display the strongest effect of projectional distortion. Additionally we choose four species with entirely continental distributions to investigate the effect of incomplete cell coverage at the coast. We expected that including model weights proportional to the actual cell area should compensate for the observed bias in model coefficients, and similarly that using land coverage of a cell should decrease bias in species with coastal distribution. As anticipated, model coefficients were different between long‐lat and equal‐area projections. Having progressively smaller and a higher number of cells with increasing latitude influenced the importance of parameters in models, increased the sample size for the northernmost parts of species ranges, and reduced the subcell variability of those areas. However, this bias could be largely removed by weighting long‐lat cells by the area they cover, and marginally by correcting for land coverage. Overall we found little effect of using long‐lat rather than equal‐area projections in our analysis. The fitted relationship between environmental parameters and occurrence probability differed only very little between the two projection types. We still recommend using equal‐area projections to avoid possible bias. More importantly, our results suggest that the cell area and the proportion of a cell covered by land should be used as a weight when analyzing distribution of terrestrial species.

Predicting future distributions of lanternfish, a significant ecological resource within the Southern Ocean

“…Before quantifying changes between present and future distributions, outputs were re‐projected to the South Pole Lambert Azimuthal Equal Area projection in order to avoid potential bias of unequal cell sizes (Budic, Didenko, & Dormann, ). Two biogeographical metrics, centroid latitude and suitable habitat area, were calculated for each species both under present and future conditions using the Calculate Geometry tool of ArcGIS v. 10.5.1.…”

“…The resulting maps of distribution change were summed to visualize the spatial variability in the projected change for each species. Under RCP 8.5, this created an index of agreement ranging from −8 (maximum agreement of a decrease in habitat suitability across all ESMs) to +8 (maximum agreement of an increase in habitat suitability across all ESMs) and from −5 to +5 under RCP 4.5.Before quantifying changes between present and future distributions, outputs were re-projected to the South Pole Lambert Azimuthal Equal Area projection in order to avoid potential bias of unequal cell sizes(Budic, Didenko, & Dormann, 2016). Two biogeo-graphical metrics, centroid latitude and suitable habitat area, were calculated for each species both under present and future conditions using the Calculate Geometry tool of ArcGIS v. 10.5.1.…”

ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling

“…A1). All occurrence data and rasters were transformed and projected to the North America Albers Equal Area Conic projection, as it has been shown that a failure to account for changing grid‐cell area across latitudes can negatively impact SDM results (Budic et al ). We statistically thinned variables to include in each model for each species using the ‘corSelect’ function in the fuzzySim package ver.…”

Global inequities and political borders challenge nature conservation under climate change

“…This resulted in a final set of 3,840 mammal and 8,918 bird species—78.2% of the original species. All modeling was done in a cylindrical equal area projection to avoid biasing the models by oversampling high latitudes ( 55 ). For each species, 1,000 pseudoabsence points were randomly sampled from the same zoogeographic realm(s) ( 56 ) in which the species was found.…”